elmNNRcpp (version 1.0.4)

elm: Fit an extreme learning model

Description

Formula interface for elm_train, transforms a data frame and formula into the necessary input for elm_train, automatically calls onehot_encode for classification.

Usage

elm(
  formula,
  data,
  nhid,
  actfun,
  init_weights = "normal_gaussian",
  bias = FALSE,
  moorep_pseudoinv_tol = 0.01,
  leaky_relu_alpha = 0,
  seed = 1,
  verbose = FALSE
)

Arguments

formula

formula used to specify the regression or classification.

data

data.frame with the data

nhid

a numeric value specifying the hidden neurons. Must be >= 1

actfun

a character string specifying the type of activation function. It should be one of the following : 'sig' ( sigmoid ), 'sin' ( sine ), 'radbas' ( radial basis ), 'hardlim' ( hard-limit ), 'hardlims' ( symmetric hard-limit ), 'satlins' ( satlins ), 'tansig' ( tan-sigmoid ), 'tribas' ( triangular basis ), 'relu' ( rectifier linear unit ) or 'purelin' ( linear )

init_weights

a character string spcecifying the distribution from which the input-weights and the bias should be initialized. It should be one of the following : 'normal_gaussian' (normal / Gaussian distribution with zero mean and unit variance), 'uniform_positive' ( in the range [0,1] ) or 'uniform_negative' ( in the range [-1,1] )

bias

either TRUE or FALSE. If TRUE then bias weights will be added to the hidden layer

moorep_pseudoinv_tol

a numeric value. See the references web-link for more details on Moore-Penrose pseudo-inverse and specifically on the pseudo inverse tolerance value

leaky_relu_alpha

a numeric value between 0.0 and 1.0. If 0.0 then a simple relu ( f(x) = 0.0 for x < 0, f(x) = x for x >= 0 ) activation function will be used, otherwise a leaky-relu ( f(x) = alpha * x for x < 0, f(x) = x for x >= 0 ). It is applicable only if actfun equals to 'relu'

seed

a numeric value specifying the random seed. Defaults to 1

verbose

a boolean. If TRUE then information will be printed in the console

Value

elm object which can be used with predict, residuals and fitted.

Examples

# NOT RUN {
elm(Species ~ ., data = iris, nhid = 20, actfun="sig")

mod_elm <- elm(Species ~ ., data = iris, nhid = 20, actfun="sig")

# predict classes
predict(mod_elm, newdata = iris[1:3,-5])

# predict probabilities
predict(mod_elm, newdata = iris[1:3,-5], type="prob")

# predict elm output
predict(mod_elm, newdata = iris[1:3,-5], type="raw")

data("Boston")
elm(medv ~ ., data = Boston, nhid = 40, actfun="relu")

data("ionosphere")
elm(class ~ ., data = ionosphere, nhid=20, actfun="relu")

# }